Industrial Training IoT (Internet of Things) with Raspberry PI and AWS IoT Core Training Course

Module 0: Fundamentals & The AWS IoT Ecosystem

• Introduction to IoT
  • Defining IoT in 2024: Moving beyond "Things" to include Edge Intelligence, AI/ML at the Edge, and Cyber-Physical Systems.
  • Key drivers of IoT growth across industries and use cases.
  • Major IoT trends such as Edge Computing, Sustainability, AI/ML integration, and enhanced security.
  • The role of AWS IoT within the larger AWS ecosystem, including AWS Partner Network (APN) resources.
• Overview of the AWS IoT Service Landscape
  • AWS IoT Core (covering MQTT/Bridge, Jobs, and Device Defender).
  • AWS IoT Device Management (handling device onboarding, configuration management, and OTA updates).
  • AWS IoT Analytics (for data processing, enrichment, and modeling).
  • AWS IoT Greengrass (enabling edge compute, local execution, and secure connectivity).
  • AWS IoT Button (a conceptual overview for simple device interactions).
  • Connection: Linking AWS IoT Core to Lambda, DynamoDB, OpenSearch, Step Functions, and SageMaker.

Module 1: IoT Architecture, Components & Security

• IoT Architecture
  • Device Layer (comprising sensors, actuators, and edge devices like Raspberry Pi and ESP32).
  • Connectivity Layer (supporting protocols like MQTT, CoAP, HTTP, and LPWAN options such as LoRaWAN, NB-IoT, Sigfox, and Cellular IoT).
  • Cloud Integration Layer (utilizing AWS IoT Core, API Gateway, Lambda, and Step Functions).
  • Data Processing & Analytics Layer (using DynamoDB, Timestream, OpenSearch, S3, Athena, and SageMaker).
  • Application Layer (mobile and web apps via AWS Amplify, or custom business applications).
  • Importance: Understanding the rationale behind distributed architectures, including latency, bandwidth, compute power, and security.
• In-Depth Look at Essential IoT Components
  • Hardware: Selection criteria (MCU, connectivity, sensors) and security features (Trusted Execution Environments - TEEs).
  • Edge Computing (AWS Greengrass): Advantages such as low latency, reduced cloud traffic, and local decision-making.
  • Device Management: Covering onboarding (via Over-the-Air - OTA or pre-provisioning), configuration, monitoring, and remote debugging.
  • Security Deep Dive: Addressing device identity, authentication, and authorization (using X.509 Certs and JSON Web Tokens - JWTs), data encryption (at rest and in transit), and AWS IoT Device Defender.
  • Security Standardization: Introduction to standards (e.g., IEEE P2145, Open Connectivity Foundation - OCF) and compliance frameworks (ISO/IEC 27001, SOC 2).
• AWS-Specific PaaS Functions for IoT
  • AWS IoT Core (providing secure MQTT/Bridge, jobs for firmware updates, and Device Defender).
  • AWS Lambda (for serverless compute tasks like data preprocessing and triggering actions).
  • AWS Step Functions (managing stateful workflows for complex device interactions).
  • Amazon DynamoDB (a NoSQL database for rapid IoT data ingestion).
  • Amazon OpenSearch Service (for search, analytics, and time-series data handling).
  • Amazon Timestream (a specialized time-series database).
  • Amazon S3 (for raw data lake storage).
  • AWS IoT Device Defender (for monitoring and security assessments).
  • AWS IoT Wireless (for connecting remote LPWAN devices).

Module 2: IoT Device Communication Protocols

• MQTT (MQTT v5 & WebSockets)
  • Features of MQTT 5.0 (including Retain, Clean Session flags, User Properties, and Wildcard topics).
  • MQTT over WebSockets (standardization efforts).
  • Explanation of Quality of Service (QoS) levels.
  • Best practices for protocol implementation.
• Alternative Protocols
  • CoAP (Constrained Application Protocol) designed for constrained devices.
  • AMQP / MQTT over AMQP (standard data interchange formats).
  • HTTP (suited for simpler, less frequent updates).
  • WebSockets (enabling full-duplex communication).

Module 3: Building Robust IoT Applications with AWS

• Device Onboarding & Secure Connectivity
  • Pre-provisioning with AWS IoT Device Defender.
  • Secure Over-The-Air (OTA) onboarding (e.g., applying AWS IoT Button concepts).
  • Managing device certificates (ACM/PKI).
  • Implementing MQTT with TLS.
• Data Ingestion, Storage & Processing
  • Efficiently transmitting data from devices to AWS IoT Core.
  • Selecting the appropriate target: Lambda (event-driven), Step Functions (orchestration), Timestream (time-series), OpenSearch (search & analytics), or S3 (raw data).
  • Utilizing AWS IoT Analytics for data enrichment and cleansing before storage.
  • Managing high-throughput scenarios (using Kinesis/Firehose).
• Device Management & Operations
  • Leveraging AWS IoT Device Management for fleet oversight.
  • Implementing and managing OTA Updates (using AWS IoT Jobs).
  • Remote monitoring and configuration.
• Building the IoT Backend
  • Using API Gateway to create REST/GraphQL APIs for interacting with devices and data.
  • Employing AWS Lambda for business logic.
  • Coordinating distributed components with AWS Step Functions.
  • Using Amazon SQS/SNS for asynchronous messaging and event triggering.

Module 4: Edge Computing & Advanced Integration

• AWS IoT Greengrass
  • Core concepts (Core, Device, and Connector).
  • Executing Lambda functions locally on the device.
  • Running code directly on the device (using C++ or Python).
  • Ensuring secure communication between Greengrass Core and AWS/IoT devices.
  • Use Case: Performing local data filtering, preprocessing, or AI inference at the edge.
• Integration with AI/ML
  • Using SageMaker for complex ML models in the cloud.
  • Running ML inference on the edge with the Greengrass ML Accelerator (GMA).
• Data Visualization & User Interfaces
  • Utilizing AWS IoT SiteWise for industrial data visualization.
  • Building Web Apps with AWS Amplify (including API, UI, and Authentication).
  • Creating dashboards using Amazon QuickSight or OpenSearch Dashboards.

Module 5: Security, Governance & Best Practices

• IoT Security Lifecycle
  • Secure Design Principles (focusing on Defense-in-Depth).
  • Secure Development Practices (aligned with OWASP IoT Top 10).
  • Vulnerability Management.
  • Threat Modeling specific to IoT.
• AWS Security Services for IoT
  • AWS IoT Device Defender (both Service and Device levels).
  • AWS Shield and AWS Identity and Access Management (IAM).
  • Using AWS Config for compliance checks.
  • Integration of Hardware Security Modules (HSMs).
• Data Privacy & Governance
  • Handling sensitive data (including PII).
  • Data retention and deletion policies.
  • Compliance considerations.

Module 6: Hands-on Projects & Capstone

• Guided Hands-on Labs
  • Device Onboarding & MQTT Communication.
  • Implementing Secure Data Ingestion to AWS.
  • Building a Simple IoT Dashboard.
  • OTA Update Simulation.
  • Introduction to AWS IoT Greengrass.
• Capstone Project
  • Developing a complete IoT solution addressing a real-world challenge (e.g., Smart Home Automation, Environmental Monitoring, or an Industrial Sensor Hub).
  • Requirements: Including secure device connectivity, data ingestion, processing, visualization, and an optional edge component.
  • Utilizing the AWS services covered throughout the course.